CONSORT flow

consort <- readRDS(here("data", "completion_summary.rds")) %>% 
  spread(reason, n) %>% 
  mutate(group = 1:n(),
         assigned = Approved + `Failed first attention check`,
         issue = str_replace_all(issue, "assistance", "assist.")) %>% 
  mutate(assigned_label = glue("Allocated to Group {group}\n{crackdown}\n{issue}\n{funding} funding\n\nN = {assigned}"),
         completed_label = glue("Completed\nN = {Approved}\n\n{`Failed first attention check`} failed\nattention check"))

assessed_eligibility_n <- sum(consort$Approved, consort$`Failed first attention check`, 
                              consort$`Took survey outisde of MTurk`)
ineligible_n <- sum(consort$`Took survey outisde of MTurk`)
randomized_n <- sum(consort$Approved, consort$`Failed first attention check`)


# https://aghaynes.wordpress.com/2018/05/09/flow-charts-in-r/
# set some parameters to use repeatedly
width <- 0.1
xs <- seq(0.1, 0.9, length.out = 8)
allocated_y <- 0.375
completed_y <- 0.125

box_gp_grey <- gpar(fill = ngo_cols("light grey"))
box_gp_blue_dk <- gpar(fill = ngo_cols("blue"), alpha = 0.75)
box_gp_blue_lt <- gpar(fill = ngo_cols("blue"), alpha = 0.35)
box_gp_green <- gpar(fill = ngo_cols("green"), alpha = 0.65)
box_gp_yellow <- gpar(fill = ngo_cols("yellow"))
box_gp_orange <- gpar(fill = ngo_cols("orange"), alpha = 0.65)

txt_gp <- gpar(fontfamily = "Encode Sans Condensed Medium", 
               fontface = "plain", fontsize = 8)

# Create boxes
total <- boxGrob(glue("Assessed for eligibility\n N = {assessed_eligibility_n}"), 
                 x = 0.5, y = 0.9, width = 2 * width,
                 box_gp = box_gp_blue_lt, txt_gp = txt_gp)
randomized <- boxGrob(glue("Randomized\n N = {randomized_n}"), 
                      x = 0.5, y = 0.65, width = 2 * width,
                      box_gp = box_gp_blue_dk, txt_gp = txt_gp)
ineligible <- boxGrob(glue("Participants excluded for\ncompleting Qualtrics survey\noutside of MTurk\n N = {ineligible_n}"), 
                      x = xs[7], y = 0.775, #width = 0.25,
                      box_gp = box_gp_yellow, txt_gp = txt_gp)

group1 <- boxGrob(filter(consort, group == 1)$assigned_label,
                  x = xs[1], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)
group2 <- boxGrob(filter(consort, group == 2)$assigned_label,
                  x = xs[2], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)
group3 <- boxGrob(filter(consort, group == 3)$assigned_label,
                  x = xs[3], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)
group4 <- boxGrob(filter(consort, group == 4)$assigned_label,
                  x = xs[4], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)
group5 <- boxGrob(filter(consort, group == 5)$assigned_label,
                  x = xs[5], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)
group6 <- boxGrob(filter(consort, group == 6)$assigned_label,
                  x = xs[6], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)
group7 <- boxGrob(filter(consort, group == 7)$assigned_label,
                  x = xs[7], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)
group8 <- boxGrob(filter(consort, group == 8)$assigned_label,
                  x = xs[8], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)

group1_completed <- boxGrob(filter(consort, group == 1)$completed_label, 
                            x = xs[1], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)
group2_completed <- boxGrob(filter(consort, group == 2)$completed_label, 
                            x = xs[2], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)
group3_completed <- boxGrob(filter(consort, group == 3)$completed_label, 
                            x = xs[3], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)
group4_completed <- boxGrob(filter(consort, group == 4)$completed_label, 
                            x = xs[4], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)
group5_completed <- boxGrob(filter(consort, group == 5)$completed_label, 
                            x = xs[5], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)
group6_completed <- boxGrob(filter(consort, group == 6)$completed_label, 
                            x = xs[6], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)
group7_completed <- boxGrob(filter(consort, group == 7)$completed_label, 
                            x = xs[7], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)
group8_completed <- boxGrob(filter(consort, group == 8)$completed_label, 
                            x = xs[8], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)

total_random_connect <- connectGrob(total, randomized, "v")
total_ineligible_connect <- connectGrob(total, ineligible, "-")

rand_connect1 <- connectGrob(randomized, group1, "N")
rand_connect2 <- connectGrob(randomized, group2, "N")
rand_connect3 <- connectGrob(randomized, group3, "N")
rand_connect4 <- connectGrob(randomized, group4, "N")
rand_connect5 <- connectGrob(randomized, group5, "N")
rand_connect6 <- connectGrob(randomized, group6, "N")
rand_connect7 <- connectGrob(randomized, group7, "N")
rand_connect8 <- connectGrob(randomized, group8, "N")

complete_connect1 <- connectGrob(group1, group1_completed, "N")
complete_connect2 <- connectGrob(group2, group2_completed, "N")
complete_connect3 <- connectGrob(group3, group3_completed, "N")
complete_connect4 <- connectGrob(group4, group4_completed, "N")
complete_connect5 <- connectGrob(group5, group5_completed, "N")
complete_connect6 <- connectGrob(group6, group6_completed, "N")
complete_connect7 <- connectGrob(group7, group7_completed, "N")
complete_connect8 <- connectGrob(group8, group8_completed, "N")

full_chart <- list(total, randomized, ineligible, total_random_connect, total_ineligible_connect,
                   group1, group2, group3, group4, group5, group6, group7, group8,
                   rand_connect1, rand_connect2, rand_connect3, rand_connect4, 
                   rand_connect5, rand_connect6, rand_connect7, rand_connect8,
                   group1_completed, group2_completed, group3_completed, group4_completed, 
                   group5_completed, group6_completed, group7_completed, group8_completed,
                   complete_connect1, complete_connect2, complete_connect3, complete_connect4, 
                   complete_connect5, complete_connect6, complete_connect7, complete_connect8) 

Characteristics of experiment samples

We compare our sample with demographic characteristics of the general population. Since there is no nationally representative sample for each of our demographic variables, we use two waves of the US Census’s Current Population Survey (CPS), with data from the Minnesota Population Center’s Integrated Public Use Microdata Series (IPUMS).

For general demographic information, we use data from the 2017 Annual Social and Economic Supplement (ASEC) for the CPS. From 2002–2015, the CPS included a Volunteer Supplement every September, so we use 2015 data for data on volunteering and donating to charity.

IPUMS requires that you manually generate a data extract through their website, so downloading data from them is not entirely automated or reproducible. We created two extracts (though this could have been combined into one), with the following variables

  • "data/ipums-cps/cps_2017.dat.gz": 2017 ASEC, with the following variables selected (in addition to whatever IPUMS preselects by default) (and weighted by ASECWT):
    • AGE
    • SEX
    • EDUC
    • INCTOT
  • "data/ipums-cps/cps_09_2015.dat.gz": September 2015 basic monthly CPS (which includes the Volunteer Supplement), with the following variables selected (and weighted by VLSUPPWT):
    • VLSTATUS
    • VLDONATE

We do not show other respondent demographic details because we don’t have good population-level data to compare our sample with. We could theoretically use Pew data for political preferences, but they collect data on party affiliation, while we collected data about respondent positions along a conservative–liberal spectrum, so the two variables aren’t comparable.

Characteristics of experimental sample {#tbl:exp-sample}
Variable Sample National median 90% HPDI
Female (%)a 54.4% 51.0% 3.4% (-0.1%, 6.9%)
Age (% 35+)a 48.0% 53.9% -6.0% (-9.9%, -2.6%)
Income (% $50,000+)a 50.7% 27.4% 22.0% (18.2%, 25.3%)
Education (% BA+)a 45.8% 29.9% 16.0% (12.8%, 19.6%)
Donated in past year (%)b 82.4% 48.8% 33.6% (30.9%, 36.2%)
Volunteered in past year (%)b 54.2% 75.1% -20.9% (-24.2%, -17.3%)
aAnnual CPS, March 2017
bMonthly CPS, September 2015
National value is outside the sample highest posterior density interval (HPDI)

Miscellaneous survey details

Average time to complete survey

Statistic Minutes
Minimum 00:42
Maximum 17:34
Mean 03:21
Standard deviation 02:04
Median 02:48

Amount donated (full)

Models

(1) (2) (3) (4) (5) (6)
Intercept 20.207  20.641  22.033  4.324  4.740  6.146 
(1.597) (2.239) (3.049) (4.514) (4.938) (5.207)
Crackdown (yes) 3.797  2.636  -2.692  3.178  2.349  -2.676 
(2.254) (3.055) (4.337) (2.108) (3.057) (4.227)
Issue (humanitarian)       -0.993  -4.030        -1.138  -3.359 
      (3.094) (4.312)       (3.059) (4.245)
Funding (private)             -2.867              -2.108 
            (4.444)             (4.279)
Crackdown × Issue       2.418  15.090        1.851  13.427 
      (4.366) (5.969)       (4.263) (6.023)
Crackdown × Funding             10.831              9.920 
            (6.274)             (6.090)
Issue × Funding             6.595              4.747 
            (6.013)             (6.082)
Crackdown × Issue × Funding             -25.544              -22.980 
            (8.628)             (8.702)
Prior favorability towards humanitarian NGOs                   1.220  1.340  1.177 
                  (3.691) (3.739) (3.762)
Give to charity once a month–once a year                   8.650  8.667  8.608 
                  (3.184) (3.150) (3.083)
Give to charity at least once a month                   7.435  7.470  7.388 
                  (3.696) (3.875) (3.669)
Volunteered in past year                   5.480  5.519  5.230 
                  (2.293) (2.359) (2.264)
Follow current political evens often                   -2.809  -2.728  -2.152 
                  (2.798) (2.933) (2.822)
Liberal political views                   8.270  8.267  8.105 
                  (2.340) (2.291) (2.386)
Bachelor’s degree or higher                   -2.445  -2.536  -2.464 
                  (2.218) (2.263) (2.236)
Attend religious services at least once a month                   9.817  9.833  9.230 
                  (2.696) (2.724) (2.796)
Income $50,000 or higher                   2.342  2.306  2.114 
                  (2.173) (2.286) (2.252)
Age 35 or higher                   1.924  1.974  1.693 
                  (2.199) (2.265) (2.302)
Observations 546      546      546      530      530      530     
Posterior sample size 4000.000  4000.000  4000.000  4000.000  4000.000  4000.000 
Sigma 25.517  25.569  25.386  24.699  24.746  24.549 
.

Coefficient plot

posterior_details <- data_frame(model_name = c("Basic model", "Full model"),
                                model = list(m_amount_cif,
                                             m_amount_cif_full)) %>% 
  mutate(posterior = model %>% map(~ as_data_frame(.)),
         tidy_summary = model %>% map(~ tidyMCMC(., conf.int = TRUE, conf.level = 0.9,
                                                 conf.method = "HPDinterval"))) 

coefs_posterior <- posterior_details %>%
  unnest(posterior) %>% 
  gather(term, value, -model_name) %>% 
  left_join(clean_coefs, by = "term") %>% 
  filter(term_clean != "Intercept") %>% 
  mutate(full_model = !simple_model)

coefs_summary <- posterior_details %>% 
  unnest(tidy_summary) %>% 
  left_join(clean_coefs, by = "term") %>% 
  filter(term_clean != "Intercept") %>% 
  mutate(full_model = !simple_model)

amount_coefs_full <- ggplot(coefs_posterior, aes(x = value, y = fct_rev(term_clean_fct))) +
  stat_density_ridges(aes(fill = model_name), alpha = 0.6, color = "black",
                      rel_min_height = 0.01, scale = 1.5, 
                      quantile_lines = TRUE, quantiles = 2) +
  geom_segment(data = coefs_summary, 
               aes(x = conf.low, xend = conf.high, 
                   y = fct_rev(term_clean_fct), yend = fct_rev(term_clean_fct)),
               size = 1) +
  geom_vline(xintercept = 0, linetype = "dotted", size = 1) + 
  scale_fill_manual(values = ngo_cols("red", "blue", name = FALSE), name = NULL) +
  labs(x = "Posterior median estimate + 90% credible interval", y = NULL,
       caption = "90% credible intervals shown in black. Solid vertical line = median; dotted vertical line = 0") +
  facet_wrap(~ full_model, scales = "free") + 
  theme_ngos() +
  theme(legend.position = "bottom",
        strip.text = element_blank())

amount_coefs_full %T>%
  print() %T>% 
  ggsave(., filename = here("output", "figures", "amount-coefs-full.pdf"),
         width = 8, height = 5, units = "in", device = cairo_pdf) %>% 
  ggsave(., filename = here("output", "figures", "amount-coefs-full.png"),
         width = 8, height = 5, units = "in", type = "cairo", dpi = 300)

Predicted medians across different variables

Charitable giving

Voluntarism and religiosity


Likelihood of donation (full)

Models

(1) (2) (3) (4) (5) (6)
Intercept -0.299  -0.283  -0.170  -2.241  -2.324  -2.247 
(0.120) (0.171) (0.234) (0.431) (0.489) (0.529)
Crackdown (yes) 0.140  0.020  -0.681  0.211  0.131  -0.620 
(0.166) (0.239) (0.340) (0.183) (0.254) (0.379)
Issue (humanitarian)       -0.034  -0.061        0.116  0.131 
      (0.236) (0.312)       (0.254) (0.359)
Funding (private)             -0.241              -0.219 
            (0.337)             (0.356)
Crackdown × Issue       0.227  0.898        0.175  0.883 
      (0.335) (0.452)       (0.360) (0.516)
Crackdown × Funding             1.399              1.462 
            (0.464)             (0.518)
Issue × Funding             0.086              0.010 
            (0.462)             (0.492)
Crackdown × Issue × Funding             -1.343              -1.405 
            (0.641)             (0.710)
Prior favorability towards humanitarian NGOs                   0.619  0.621  0.635 
                  (0.346) (0.357) (0.373)
Give to charity once a month–once a year                   1.722  1.728  1.759 
                  (0.318) (0.318) (0.325)
Give to charity at least once a month                   1.593  1.609  1.646 
                  (0.364) (0.359) (0.377)
Volunteered in past year                   -0.128  -0.149  -0.149 
                  (0.201) (0.199) (0.205)
Follow current political evens often                   -0.277  -0.256  -0.245 
                  (0.249) (0.259) (0.257)
Liberal political views                   0.630  0.637  0.653 
                  (0.203) (0.200) (0.203)
Bachelor’s degree or higher                   -0.101  -0.104  -0.114 
                  (0.203) (0.198) (0.193)
Attend religious services at least once a month                   0.257  0.238  0.216 
                  (0.228) (0.235) (0.227)
Income $50,000 or higher                   -0.067  -0.066  -0.081 
                  (0.191) (0.201) (0.197)
Age 35 or higher                   -0.325  -0.330  -0.376 
                  (0.197) (0.195) (0.199)
Observations 546      546      546      530      530      530     
Posterior sample size 4000.000  4000.000  4000.000  4000.000  4000.000  4000.000 
Sigma 1.000  1.000  1.000  1.000  1.000  1.000 
.

Coefficient plot

posterior_details_likely <- data_frame(model_name = c("Basic model", "Full model"),
                                       model = list(m_likely_cif,
                                                    m_likely_cif_full)) %>% 
  mutate(posterior = model %>% map(~ as_data_frame(.)),
         tidy_summary = model %>% map(~ tidyMCMC(., conf.int = TRUE, conf.level = 0.9,
                                                 conf.method = "HPDinterval"))) 

coefs_posterior_likely <- posterior_details_likely %>%
  unnest(posterior) %>% 
  gather(term, value, -model_name) %>% 
  left_join(clean_coefs, by = "term") %>% 
  filter(term_clean != "Intercept") %>% 
  mutate(full_model = !simple_model)

coefs_summary_likely <- posterior_details_likely %>% 
  unnest(tidy_summary) %>% 
  left_join(clean_coefs, by = "term") %>% 
  filter(term_clean != "Intercept") %>% 
  mutate(full_model = !simple_model)

likely_coefs_full <- ggplot(coefs_posterior_likely, aes(x = value, y = fct_rev(term_clean_fct))) +
  stat_density_ridges(aes(fill = model_name), alpha = 0.6, color = "black",
                      rel_min_height = 0.01, scale = 1.5, 
                      quantile_lines = TRUE, quantiles = 2) +
  geom_segment(data = coefs_summary_likely, 
               aes(x = conf.low, xend = conf.high, 
                   y = fct_rev(term_clean_fct), yend = fct_rev(term_clean_fct)),
               size = 1) +
  geom_vline(xintercept = 0, linetype = "dotted", size = 1) + 
  scale_fill_manual(values = ngo_cols("red", "blue", name = FALSE), name = NULL) +
  labs(x = "Posterior median estimate + 90% credible interval", y = NULL,
       caption = "90% credible intervals shown in black. Solid vertical line = median; dotted vertical line = 0") +
  facet_wrap(~ full_model, scales = "free") + 
  theme_ngos() +
  theme(legend.position = "bottom",
        strip.text = element_blank())

likely_coefs_full %T>%
  print() %T>% 
  ggsave(., filename = here("output", "figures", "likely-coefs-full.pdf"),
         width = 8, height = 5, units = "in", device = cairo_pdf) %>% 
  ggsave(., filename = here("output", "figures", "likely-coefs-full.png"),
         width = 8, height = 5, units = "in", type = "cairo", dpi = 300)

Original computing environment

## # http://dirk.eddelbuettel.com/blog/2017/11/27/#011_faster_package_installation_one
## VER=
## CCACHE=ccache
## CC=$(CCACHE) gcc$(VER)
## CXX=$(CCACHE) g++$(VER)
## CXXFLAGS=-Wno-unused-variable -Wno-unused-function -Wno-unused-local-typedefs
## CXX11=$(CCACHE) g++$(VER)
## CXX14=$(CCACHE) g++$(VER)
## FLIBS = -L`gfortran -print-file-name=libgfortran.dylib | xargs dirname`
## FC=$(CCACHE) gfortran$(VER)
## F77=$(CCACHE) gfortran$(VER)
## Session info -------------------------------------------------------------
##  setting  value                       
##  version  R version 3.5.1 (2018-07-02)
##  system   x86_64, darwin15.6.0        
##  ui       X11                         
##  language (EN)                        
##  collate  en_US.UTF-8                 
##  tz       America/Denver              
##  date     2018-07-19
## Packages -----------------------------------------------------------------
##  package      * version    date       source                              
##  abind          1.4-5      2016-07-21 CRAN (R 3.5.0)                      
##  acepack        1.4.1      2016-10-29 CRAN (R 3.5.0)                      
##  assertthat     0.2.0      2017-04-11 CRAN (R 3.5.0)                      
##  backports      1.1.2      2017-12-13 CRAN (R 3.5.0)                      
##  base         * 3.5.1      2018-07-05 local                               
##  base64enc      0.1-3      2015-07-28 CRAN (R 3.5.0)                      
##  bayesplot      1.5.0      2018-03-30 CRAN (R 3.5.0)                      
##  bindr          0.1.1      2018-03-13 CRAN (R 3.5.0)                      
##  bindrcpp     * 0.2.2      2018-03-29 CRAN (R 3.5.0)                      
##  broom        * 0.4.5      2018-07-03 CRAN (R 3.5.0)                      
##  cellranger     1.1.0      2016-07-27 CRAN (R 3.5.0)                      
##  checkmate      1.8.5      2017-10-24 CRAN (R 3.5.0)                      
##  cli            1.0.0      2017-11-05 CRAN (R 3.5.0)                      
##  cluster        2.0.7-1    2018-04-13 CRAN (R 3.5.1)                      
##  coda           0.19-1     2016-12-08 CRAN (R 3.5.0)                      
##  codetools      0.2-15     2016-10-05 CRAN (R 3.5.1)                      
##  colorspace     1.3-2      2016-12-14 CRAN (R 3.5.0)                      
##  colourpicker   1.0        2017-09-27 CRAN (R 3.5.0)                      
##  compiler       3.5.1      2018-07-05 local                               
##  crayon         1.3.4      2017-09-16 CRAN (R 3.5.0)                      
##  crosstalk      1.0.0      2016-12-21 CRAN (R 3.5.0)                      
##  data.table     1.10.4-3   2017-10-27 CRAN (R 3.5.0)                      
##  datasets     * 3.5.1      2018-07-05 local                               
##  devtools       1.13.5     2018-02-18 CRAN (R 3.5.0)                      
##  digest         0.6.15     2018-01-28 CRAN (R 3.5.0)                      
##  dplyr        * 0.7.6      2018-06-29 CRAN (R 3.5.1)                      
##  DT             0.4        2018-01-30 CRAN (R 3.5.0)                      
##  dygraphs       1.1.1.4    2017-01-04 CRAN (R 3.5.0)                      
##  evaluate       0.10.1     2017-06-24 CRAN (R 3.5.0)                      
##  forcats      * 0.3.0      2018-02-19 CRAN (R 3.5.0)                      
##  foreign        0.8-70     2017-11-28 CRAN (R 3.5.1)                      
##  forestplot     1.7.2      2017-09-16 CRAN (R 3.5.0)                      
##  Formula        1.2-2      2017-07-10 CRAN (R 3.5.0)                      
##  ggplot2      * 3.0.0      2018-07-03 CRAN (R 3.5.0)                      
##  ggridges     * 0.5.0      2018-04-05 CRAN (R 3.5.0)                      
##  ggstance     * 0.3        2016-11-16 CRAN (R 3.5.0)                      
##  glue         * 1.2.0.9000 2018-04-30 Github (tidyverse/glue@b538962)     
##  Gmisc        * 1.6.1      2018-04-21 CRAN (R 3.5.0)                      
##  graphics     * 3.5.1      2018-07-05 local                               
##  grDevices    * 3.5.1      2018-07-05 local                               
##  grid         * 3.5.1      2018-07-05 local                               
##  gridExtra    * 2.3        2017-09-09 CRAN (R 3.5.0)                      
##  gtable         0.2.0      2016-02-26 CRAN (R 3.5.0)                      
##  gtools         3.5.0      2015-05-29 CRAN (R 3.5.0)                      
##  haven          1.1.2      2018-06-27 CRAN (R 3.5.0)                      
##  here         * 0.1        2017-05-28 CRAN (R 3.5.0)                      
##  Hmisc          4.1-1      2018-01-03 CRAN (R 3.5.0)                      
##  hms            0.4.2      2018-03-10 CRAN (R 3.5.0)                      
##  htmlTable    * 1.11.2     2018-01-20 CRAN (R 3.5.0)                      
##  htmltools      0.3.6      2017-04-28 CRAN (R 3.5.0)                      
##  htmlwidgets    1.2        2018-04-19 CRAN (R 3.5.0)                      
##  httpuv         1.4.3      2018-05-10 cran (@1.4.3)                       
##  httr           1.3.1      2017-08-20 CRAN (R 3.5.0)                      
##  huxtable     * 4.0.1      2018-07-03 CRAN (R 3.5.0)                      
##  igraph         1.2.1      2018-03-10 CRAN (R 3.5.0)                      
##  inline         0.3.14     2015-04-13 CRAN (R 3.5.0)                      
##  ipumsr       * 0.2.0      2018-04-20 CRAN (R 3.5.0)                      
##  jsonlite       1.5        2017-06-01 CRAN (R 3.5.0)                      
##  knitr          1.20       2018-02-20 CRAN (R 3.5.0)                      
##  labeling       0.3        2014-08-23 CRAN (R 3.5.0)                      
##  later          0.7.3      2018-06-08 cran (@0.7.3)                       
##  lattice        0.20-35    2017-03-25 CRAN (R 3.5.1)                      
##  latticeExtra   0.6-28     2016-02-09 CRAN (R 3.5.0)                      
##  lazyeval       0.2.1      2017-10-29 CRAN (R 3.5.0)                      
##  lme4           1.1-17     2018-04-03 CRAN (R 3.5.0)                      
##  loo            2.0.0      2018-04-11 CRAN (R 3.5.0)                      
##  lubridate    * 1.7.4      2018-04-11 CRAN (R 3.5.0)                      
##  magrittr     * 1.5        2014-11-22 CRAN (R 3.5.0)                      
##  markdown       0.8        2017-04-20 CRAN (R 3.5.0)                      
##  MASS           7.3-50     2018-04-30 CRAN (R 3.5.1)                      
##  Matrix         1.2-14     2018-04-13 CRAN (R 3.5.1)                      
##  matrixStats    0.53.1     2018-02-11 CRAN (R 3.5.0)                      
##  memoise        1.1.0      2017-04-21 CRAN (R 3.5.0)                      
##  methods      * 3.5.1      2018-07-05 local                               
##  mime           0.5        2016-07-07 CRAN (R 3.5.0)                      
##  miniUI         0.1.1      2016-01-15 CRAN (R 3.5.0)                      
##  minqa          1.2.4      2014-10-09 CRAN (R 3.5.0)                      
##  mnormt         1.5-5      2016-10-15 CRAN (R 3.5.0)                      
##  modelr       * 0.1.2      2018-05-11 CRAN (R 3.5.0)                      
##  munsell        0.5.0      2018-06-12 cran (@0.5.0)                       
##  nlme           3.1-137    2018-04-07 CRAN (R 3.5.1)                      
##  nloptr         1.0.4      2017-08-22 CRAN (R 3.5.0)                      
##  nnet           7.3-12     2016-02-02 CRAN (R 3.5.1)                      
##  pander       * 0.6.1      2017-08-06 CRAN (R 3.5.0)                      
##  parallel       3.5.1      2018-07-05 local                               
##  patchwork    * 0.0.1      2018-07-16 Github (thomasp85/patchwork@7fb35b1)
##  pillar         1.2.3      2018-05-25 CRAN (R 3.5.0)                      
##  pkgconfig      2.0.1      2017-03-21 CRAN (R 3.5.0)                      
##  plyr           1.8.4      2016-06-08 CRAN (R 3.5.0)                      
##  promises       1.0.1      2018-04-13 CRAN (R 3.5.0)                      
##  psych          1.8.4      2018-05-06 cran (@1.8.4)                       
##  purrr        * 0.2.5      2018-05-29 cran (@0.2.5)                       
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##  RColorBrewer   1.1-2      2014-12-07 CRAN (R 3.5.0)                      
##  Rcpp         * 0.12.17    2018-05-18 cran (@0.12.17)                     
##  readr        * 1.1.1      2017-05-16 CRAN (R 3.5.0)                      
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##  rprojroot      1.3-2      2018-01-03 CRAN (R 3.5.0)                      
##  rsconnect      0.8.8      2018-03-09 CRAN (R 3.5.0)                      
##  rstan        * 2.17.3     2018-01-20 CRAN (R 3.5.0)                      
##  rstanarm     * 2.17.4     2018-04-13 CRAN (R 3.5.0)                      
##  rstantools     1.5.0      2018-04-17 CRAN (R 3.5.0)                      
##  rstudioapi     0.7        2017-09-07 CRAN (R 3.5.0)                      
##  rvest          0.3.2      2016-06-17 CRAN (R 3.5.0)                      
##  scales       * 0.5.0.9000 2018-07-16 Github (hadley/scales@419236a)      
##  shiny          1.0.5      2017-08-23 CRAN (R 3.5.0)                      
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##  shinystan      2.5.0      2018-05-01 CRAN (R 3.5.0)                      
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##  splines        3.5.1      2018-07-05 local                               
##  StanHeaders  * 2.17.2     2018-01-20 CRAN (R 3.5.0)                      
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##  stringi        1.2.3      2018-06-12 CRAN (R 3.5.0)                      
##  stringr      * 1.3.1      2018-05-10 CRAN (R 3.5.0)                      
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##  threejs        0.3.1      2017-08-13 CRAN (R 3.5.0)                      
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##  tidyverse    * 1.2.1      2017-11-14 CRAN (R 3.5.0)                      
##  tools          3.5.1      2018-07-05 local                               
##  utils        * 3.5.1      2018-07-05 local                               
##  withr          2.1.2      2018-07-16 Github (jimhester/withr@fe56f20)    
##  XML            3.98-1.11  2018-04-16 CRAN (R 3.5.0)                      
##  xml2           1.2.0      2018-01-24 CRAN (R 3.5.0)                      
##  xtable         1.8-2      2016-02-05 CRAN (R 3.5.0)                      
##  xts            0.10-2     2018-03-14 CRAN (R 3.5.0)                      
##  yaml           2.1.19     2018-05-01 CRAN (R 3.5.0)                      
##  zeallot        0.1.0      2018-01-28 CRAN (R 3.5.0)                      
##  zoo            1.8-1      2018-01-08 CRAN (R 3.5.0)
---
title: "Additional analysis"
author: "Andrew Heiss and Suparna Chaudhry"
date: "Last run: `r format(Sys.time(), '%B %e, %Y')`"
output: 
  html_document:
    code_folding: hide
editor_options: 
  chunk_output_type: console
---

```{r load-libraries-data, warning=FALSE, message=FALSE}
# Load libraries
library(tidyverse)
library(magrittr)
library(rstan)
library(rstanarm)
library(broom)
library(glue)
library(ggstance)
library(ggridges)
library(grid)
library(gridExtra)
library(Gmisc)
library(patchwork)
library(pander)
library(scales)
library(huxtable)
library(lubridate)
library(modelr)
library(ipumsr)
library(here)

source(here("lib", "graphics.R"))
source(here("lib", "pander_options.R"))
source(here("lib", "modeling.R"))

# Load data
results <- readRDS(here("data", "results_clean.rds"))
```

# CONSORT flow

```{r build-consort}
consort <- readRDS(here("data", "completion_summary.rds")) %>% 
  spread(reason, n) %>% 
  mutate(group = 1:n(),
         assigned = Approved + `Failed first attention check`,
         issue = str_replace_all(issue, "assistance", "assist.")) %>% 
  mutate(assigned_label = glue("Allocated to Group {group}\n{crackdown}\n{issue}\n{funding} funding\n\nN = {assigned}"),
         completed_label = glue("Completed\nN = {Approved}\n\n{`Failed first attention check`} failed\nattention check"))

assessed_eligibility_n <- sum(consort$Approved, consort$`Failed first attention check`, 
                              consort$`Took survey outisde of MTurk`)
ineligible_n <- sum(consort$`Took survey outisde of MTurk`)
randomized_n <- sum(consort$Approved, consort$`Failed first attention check`)


# https://aghaynes.wordpress.com/2018/05/09/flow-charts-in-r/
# set some parameters to use repeatedly
width <- 0.1
xs <- seq(0.1, 0.9, length.out = 8)
allocated_y <- 0.375
completed_y <- 0.125

box_gp_grey <- gpar(fill = ngo_cols("light grey"))
box_gp_blue_dk <- gpar(fill = ngo_cols("blue"), alpha = 0.75)
box_gp_blue_lt <- gpar(fill = ngo_cols("blue"), alpha = 0.35)
box_gp_green <- gpar(fill = ngo_cols("green"), alpha = 0.65)
box_gp_yellow <- gpar(fill = ngo_cols("yellow"))
box_gp_orange <- gpar(fill = ngo_cols("orange"), alpha = 0.65)

txt_gp <- gpar(fontfamily = "Encode Sans Condensed Medium", 
               fontface = "plain", fontsize = 8)

# Create boxes
total <- boxGrob(glue("Assessed for eligibility\n N = {assessed_eligibility_n}"), 
                 x = 0.5, y = 0.9, width = 2 * width,
                 box_gp = box_gp_blue_lt, txt_gp = txt_gp)
randomized <- boxGrob(glue("Randomized\n N = {randomized_n}"), 
                      x = 0.5, y = 0.65, width = 2 * width,
                      box_gp = box_gp_blue_dk, txt_gp = txt_gp)
ineligible <- boxGrob(glue("Participants excluded for\ncompleting Qualtrics survey\noutside of MTurk\n N = {ineligible_n}"), 
                      x = xs[7], y = 0.775, #width = 0.25,
                      box_gp = box_gp_yellow, txt_gp = txt_gp)

group1 <- boxGrob(filter(consort, group == 1)$assigned_label,
                  x = xs[1], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)
group2 <- boxGrob(filter(consort, group == 2)$assigned_label,
                  x = xs[2], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)
group3 <- boxGrob(filter(consort, group == 3)$assigned_label,
                  x = xs[3], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)
group4 <- boxGrob(filter(consort, group == 4)$assigned_label,
                  x = xs[4], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)
group5 <- boxGrob(filter(consort, group == 5)$assigned_label,
                  x = xs[5], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)
group6 <- boxGrob(filter(consort, group == 6)$assigned_label,
                  x = xs[6], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)
group7 <- boxGrob(filter(consort, group == 7)$assigned_label,
                  x = xs[7], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)
group8 <- boxGrob(filter(consort, group == 8)$assigned_label,
                  x = xs[8], y = allocated_y, width = width, 
                  box_gp = box_gp_orange, txt_gp = txt_gp)

group1_completed <- boxGrob(filter(consort, group == 1)$completed_label, 
                            x = xs[1], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)
group2_completed <- boxGrob(filter(consort, group == 2)$completed_label, 
                            x = xs[2], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)
group3_completed <- boxGrob(filter(consort, group == 3)$completed_label, 
                            x = xs[3], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)
group4_completed <- boxGrob(filter(consort, group == 4)$completed_label, 
                            x = xs[4], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)
group5_completed <- boxGrob(filter(consort, group == 5)$completed_label, 
                            x = xs[5], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)
group6_completed <- boxGrob(filter(consort, group == 6)$completed_label, 
                            x = xs[6], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)
group7_completed <- boxGrob(filter(consort, group == 7)$completed_label, 
                            x = xs[7], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)
group8_completed <- boxGrob(filter(consort, group == 8)$completed_label, 
                            x = xs[8], y = completed_y, width = width,
                            box_gp = box_gp_green, txt_gp = txt_gp)

total_random_connect <- connectGrob(total, randomized, "v")
total_ineligible_connect <- connectGrob(total, ineligible, "-")

rand_connect1 <- connectGrob(randomized, group1, "N")
rand_connect2 <- connectGrob(randomized, group2, "N")
rand_connect3 <- connectGrob(randomized, group3, "N")
rand_connect4 <- connectGrob(randomized, group4, "N")
rand_connect5 <- connectGrob(randomized, group5, "N")
rand_connect6 <- connectGrob(randomized, group6, "N")
rand_connect7 <- connectGrob(randomized, group7, "N")
rand_connect8 <- connectGrob(randomized, group8, "N")

complete_connect1 <- connectGrob(group1, group1_completed, "N")
complete_connect2 <- connectGrob(group2, group2_completed, "N")
complete_connect3 <- connectGrob(group3, group3_completed, "N")
complete_connect4 <- connectGrob(group4, group4_completed, "N")
complete_connect5 <- connectGrob(group5, group5_completed, "N")
complete_connect6 <- connectGrob(group6, group6_completed, "N")
complete_connect7 <- connectGrob(group7, group7_completed, "N")
complete_connect8 <- connectGrob(group8, group8_completed, "N")

full_chart <- list(total, randomized, ineligible, total_random_connect, total_ineligible_connect,
                   group1, group2, group3, group4, group5, group6, group7, group8,
                   rand_connect1, rand_connect2, rand_connect3, rand_connect4, 
                   rand_connect5, rand_connect6, rand_connect7, rand_connect8,
                   group1_completed, group2_completed, group3_completed, group4_completed, 
                   group5_completed, group6_completed, group7_completed, group8_completed,
                   complete_connect1, complete_connect2, complete_connect3, complete_connect4, 
                   complete_connect5, complete_connect6, complete_connect7, complete_connect8) 
```

```{r show-save-consort, fig.width=10, fig.height=6}
# Ordinarily, you can use grid.grab() to save the output of a grid figure into
# an object and then use that in ggsave(). However, when knitting, this creates
# a duplicate plot, which is frustrating. So instead, we use walk() to reprint
# all the grobs within specific pdf and png devices
#
# See https://stackoverflow.com/a/17509770/120898 for a similar issue

# Save as PDF
cairo_pdf(filename = here("output", "figures", "consort.pdf"),
          width = 10, height = 6)
grid.newpage()
walk(full_chart, ~ print(.))
invisible(dev.off())

# Save as PNG
png(filename = here("output", "figures", "consort.png"), 
    width = 10, height = 6, units = "in",
    bg = "white", res = 300, type = "cairo")
grid.newpage()
walk(full_chart, ~ print(.))
invisible(dev.off())

# Show in knitted document
grid.newpage()
walk(full_chart, ~ print(.))
```


# Characteristics of experiment samples

We compare our sample with demographic characteristics of the general population. Since there is no nationally representative sample for each of our demographic variables, we use two waves of the US Census's Current Population Survey (CPS), with data from the [Minnesota Population Center's Integrated Public Use Microdata Series (IPUMS)](https://cps.ipums.org/cps/).

For general demographic information, we use data from the 2017 [Annual Social and Economic Supplement (ASEC)](https://cps.ipums.org/cps/asec_sample_notes.shtml) for the CPS. From 2002–2015, the CPS included a [Volunteer Supplement](https://cps.ipums.org/cps/volunteer_sample_notes.shtml) every September, so we use 2015 data for data on volunteering and donating to charity.

IPUMS requires that you manually generate a data extract through their website, so downloading data from them is not entirely automated or reproducible. We created two extracts (though this could have been combined into one), with the following variables

-  `"data/ipums-cps/cps_2017.dat.gz"`: 2017 ASEC, with the following variables selected (in addition to whatever IPUMS preselects by default) (and weighted by `ASECWT`):
    - `AGE`
    - `SEX`
    - `EDUC`
    - `INCTOT`
-  `"data/ipums-cps/cps_09_2015.dat.gz"`: September 2015 basic monthly CPS (which includes the Volunteer Supplement), with the following variables selected (and weighted by `VLSUPPWT`):
    - `VLSTATUS`
    - `VLDONATE`

We do not show other respondent demographic details because we don't have good population-level data to compare our sample with. We could theoretically use Pew data for political preferences, but they collect data on party affiliation, while we collected data about respondent positions along a conservative–liberal spectrum, so the two variables aren't comparable.

```{r load-clean-cps, warning=FALSE, message=FALSE}
cps_2015_ddi_file <- here("data", "ipums-cps", "cps_09_2015.xml")
cps_2015_data_file <- here("data", "ipums-cps", "cps_09_2015.dat.gz")

cps_2015_ddi <- read_ipums_ddi(cps_2015_ddi_file)
cps_2015_data <- read_ipums_micro(cps_2015_ddi_file, data_file = cps_2015_data_file, verbose = FALSE)

cps_2017_ddi_file <- here("data", "ipums-cps", "cps_2017.xml")
cps_2017_data_file <- here("data", "ipums-cps", "cps_2017.dat.gz")

cps_2017_ddi <- read_ipums_ddi(cps_2017_ddi_file)
cps_2017_data <- read_ipums_micro(cps_2017_ddi_file, data_file = cps_2017_data_file, verbose = FALSE)

# Volunteering data from September 2015 only
df_volunteering <- cps_2015_data %>% 
  # Remove values not in the universe
  mutate_at(vars(VLSTATUS, VLDONATE), funs(ifelse(. == 99, NA, .)))

# All other data from annual March 2017 survey
df_demographics <- cps_2017_data %>% 
  # Remove values not in the universe
  mutate(SEX = ifelse(SEX == 9, NA, SEX),
         EDUC = ifelse(EDUC <= 1 | EDUC == 999, NA, EDUC),
         INCTOT = ifelse(INCTOT == 99999999, NA, INCTOT))
```

```{r population-values}
global_demographics <- df_demographics %>% 
  summarize(age = weighted.mean(AGE >= 35, ASECWT), 
            female = weighted.mean(SEX == 2, ASECWT),
            college = weighted.mean(EDUC >= 111, ASECWT, na.rm = TRUE),
            income = weighted.mean(INCTOT >= 50000, ASECWT, na.rm = TRUE)) %>% 
  c()

global_vol <- df_volunteering %>% 
  summarize(volunteering = weighted.mean(VLSTATUS == 2, VLSUPPWT, na.rm = TRUE),
            donating = weighted.mean(VLDONATE == 2, VLSUPPWT, na.rm = TRUE)) %>% 
  c()

global_stats <- c(global_vol, global_demographics)
```

```{r sample-population-characteristics-freq, include=FALSE, eval=FALSE}
# Demographic variables
table(results$gender_bin) %>% prop.test(., p = global_stats$female)
table(results$education_bin) %>% prop.test(., p = global_stats$college)
table(results$income_bin) %>% prop.test(., p = global_stats$income)
table(results$age_bin) %>% prop.test(., p = global_stats$age)

# Volunteering
table(results$volunteer) %>% prop.test(., p = global_stats$volunteering)
table(results$give_charity_2) %>% prop.test(., p = global_stats$donating)
```

```{r compile-stan-model, warning=FALSE, message=FALSE, cache=TRUE}
prop_test_bayes_saved <- here("lib", "bayes_prop_test.rds")

if (file.exists(prop_test_bayes_saved)) {
  prop_test_bayes <- readRDS(prop_test_bayes_saved)
} else {
  prop_test_bayes <- stan_model(here("lib", "bayes_prop_test.stan"))
  saveRDS(prop_test_bayes, prop_test_bayes_saved)
}
```

```{r sample-population-characteristics, cache=TRUE}
compare_sample_to_pop_bayes <- function(sample_value, population_value) {
  mcmc_samples <- sampling(prop_test_bayes, list(x = table(sample_value)[1],
                                                 total_n = length(sample_value),
                                                 pop_prop = population_value),
                           chains = CHAINS, iter = ITER, warmup = WARMUP, seed = BAYES_SEED)
  
  tidied <- tidyMCMC(mcmc_samples, conf.int = TRUE, conf.level = 0.9, 
                     estimate.method = "median", conf.method = "HPDinterval") %>%
    mutate(in_hpdi = (population_value >= conf.low & population_value <= conf.high))
  
  thetas <- unlist(extract(mcmc_samples, "theta"))
  pop_quantile_in_sample <- ecdf(thetas)(population_value)
  
  in_hpdi <- (population_value >= tidied[1,]$conf.low & 
                population_value <= tidied[1,]$conf.high)
  
  return(list(mcmc_samples = mcmc_samples, tidied = tidied, theta_in_hpdi = in_hpdi,
              pop_quantile_in_sample = pop_quantile_in_sample))
}

calc_sample_pop <- tribble(
  ~Variable, ~sample_value, ~National,
  "Female (%)^a^", results$gender_bin, global_stats$female,
  "Age (% 35+)^a^", results$age_bin, global_stats$age,
  "Income (% $50,000+)^a^", results$income_bin, global_stats$income,
  "Education (% BA+)^a^", results$education_bin, global_stats$college,
  "Donated in past year (%)^b^", results$give_charity_2, global_stats$donating,
  "Volunteered in past year (%)^b^", results$volunteer, global_stats$volunteering
) %>% 
  mutate(Sample = sample_value %>% map_dbl(~ prop.table(table(.))[1]),
         prop_test_bayes = map2(.x = sample_value, .y = National, 
                                .f = ~ compare_sample_to_pop_bayes(.x, .y))) 
```

```{r tbl-sample-characteristics, results="asis"}
format_hpdi <- function(post_lower, post_upper, star, digits = 1) {
  glue("({lower}%, {upper}%){star}",
       lower = round(100 * post_lower, digits),
       upper = round(100 * post_upper, digits))
}

tbl_sample_pop <- calc_sample_pop %>% 
  mutate(in_hpdi = prop_test_bayes %>% map_lgl(~ .$theta_in_hpdi),
         not_hpdi_symbol = ifelse(in_hpdi, "", "^†^"),
         diffs_tidy = prop_test_bayes %>% map(~ .$tidied[2,]),
         diffs_median = diffs_tidy %>% map_dbl(~ .$estimate),
         diffs_hpdi_fancy = diffs_tidy %>%
           map2_chr(.x = diffs_tidy, .y = not_hpdi_symbol, 
                    .f = ~ format_hpdi(.x$conf.low, .x$conf.high, .y))) %>% 
  mutate_at(vars(National, Sample, diffs_median), funs(percent)) %>% 
  select(Variable, Sample, National, 
         `∆~median~` = diffs_median,
         `90% HPDI` = diffs_hpdi_fancy)

note_row <- data_frame(Variable = c("*^a^Annual CPS, March 2017*",
                                    "*^b^Monthly CPS, September 2015*",
                                    "*^†^National value is outside the sample highest posterior density interval (HPDI)*"))

bind_rows(tbl_sample_pop, note_row) %>% 
  pandoc.table.return(keep.line.breaks = TRUE, style = "multiline", justify = "lcccc", 
                      caption = "Characteristics of experimental sample {#tbl:exp-sample}") %T>% 
  cat(file = here("output", "tables", "tbl-exp-sample.md")) %>% 
  cat()
```


# Miscellaneous survey details

## Average time to complete survey

```{r avg-time, results="asis"}
fmt_ms <- function(x) {
  n_seconds <- seconds_to_period(x)
  sprintf("%02.0f:%02.0f", minute(n_seconds), second(n_seconds))
}

theme_ngos_table <- ttheme_minimal(
  core = 
    list(fg_params = 
           list(hjust = 0, x = 0.1,
                fontsize = 7, fontface = "plain",
                fontfamily = "Encode Sans Condensed Light"),
         bg_params = list(fill = "white")),
  colhead = 
    list(fg_params = 
           list(hjust = 0, x = 0.1, col = "white",
                fontsize = 7, fontface = "plain",
                fontfamily = "Encode Sans Condensed SemiBold"),
         bg_params = list(fill = ngo_cols("blue"))),
  padding = unit(c(4, 2), "mm"))

time_summary <- results %>% 
  summarize_at(vars(duration), funs(Minimum = min, Maximum = max, Mean = mean, 
                                    `Standard deviation` = sd, Median = median)) %>% 
  gather(Statistic, value) %>% 
  mutate(Minutes = fmt_ms(value)) %>% 
  select(-value) 

pandoc.table(time_summary)
```

```{r avg-time-plot, fig.height=2.5, fig.width=4}
summary_stats <- tableGrob(time_summary, rows = NULL, theme = theme_ngos_table) %>% 
  gtable::gtable_add_grob(., grobs = rectGrob(gp = gpar(fill = NA, lwd = 1)),
                          t = 1, b = nrow(.), l = 1, r = ncol(.))

plot_avg_time <- ggplot(results, aes(x = duration)) +
  geom_histogram(bins = 50, fill = ngo_cols("blue")) +
  scale_x_time(labels = fmt_ms) +
  annotation_custom(summary_stats, xmin = 700, xmax = 900, ymin = 30, ymax = 60) +
  labs(x = "Minutes spent on experiment", y = "Count") +
  theme_ngos(base_size = 9.5) +
  theme(panel.grid.minor = element_blank())

plot_avg_time %T>% 
  print() %T>%
  ggsave(., filename = here("output", "figures", "avg-time.pdf"),
         width = 4, height = 2.25, units = "in", device = cairo_pdf) %>% 
  ggsave(., filename = here("output", "figures", "avg-time.png"),
         width = 4, height = 2.25, units = "in", type = "cairo", dpi = 300)
```

# Amount donated (full)

## Models

```{r build-models-amount-full, warning=FALSE, message=FALSE, cache=TRUE}
# Basic model
m_amount_c <- stan_glm(amount_donate ~ crackdown,
                       data = results, family = gaussian(),
                       prior = cauchy(location = 0, scale = 2.5),
                       prior_intercept = cauchy(location = 0, scale = 10),
                       chains = CHAINS, iter = ITER, warmup = WARMUP, seed = BAYES_SEED)

m_amount_ci <- update(m_amount_c, . ~ . + issue + crackdown * issue)

m_amount_cif <- update(m_amount_c, . ~ . + 
                         issue + funding + crackdown * issue + crackdown * funding + 
                         issue * funding + crackdown * issue * funding)

# Interaction models with extra controls
m_amount_c_full <- update(m_amount_c, . ~ . + 
                            favor_humanitarian_bin + 
                            give_charity_3 + volunteer + political_knowledge_bin +
                            ideology_bin + education_bin + religiosity_bin +
                            income_bin + age_bin)

m_amount_ci_full <- update(m_amount_ci, . ~ . + 
                             favor_humanitarian_bin + 
                             give_charity_3 + volunteer + political_knowledge_bin +
                             ideology_bin + education_bin + religiosity_bin +
                             income_bin + age_bin)

m_amount_cif_full <- update(m_amount_cif, . ~ . + 
                             favor_humanitarian_bin +
                             give_charity_3 + volunteer + political_knowledge_bin +
                             ideology_bin + education_bin + religiosity_bin +
                             income_bin + age_bin)
```

```{r tbl-models-amount-full, warning=FALSE, results="asis"}
huxreg(m_amount_c, m_amount_ci, m_amount_cif, 
       m_amount_c_full, m_amount_ci_full, m_amount_cif_full,
       coefs = clean_coefs_named,
       statistics = model_stats_bayes,
       stars = NULL) %T>% 
  print_hux() %>% 
  to_md(max_width = 100) %>% 
  cat(file = here("output", "tables", "tbl-models-amount-full.md"))
```

## Coefficient plot

```{r models-coefs-plot-amount, warning=FALSE, message=FALSE, fig.width=8, fig.height=5}
posterior_details <- data_frame(model_name = c("Basic model", "Full model"),
                                model = list(m_amount_cif,
                                             m_amount_cif_full)) %>% 
  mutate(posterior = model %>% map(~ as_data_frame(.)),
         tidy_summary = model %>% map(~ tidyMCMC(., conf.int = TRUE, conf.level = 0.9,
                                                 conf.method = "HPDinterval"))) 

coefs_posterior <- posterior_details %>%
  unnest(posterior) %>% 
  gather(term, value, -model_name) %>% 
  left_join(clean_coefs, by = "term") %>% 
  filter(term_clean != "Intercept") %>% 
  mutate(full_model = !simple_model)

coefs_summary <- posterior_details %>% 
  unnest(tidy_summary) %>% 
  left_join(clean_coefs, by = "term") %>% 
  filter(term_clean != "Intercept") %>% 
  mutate(full_model = !simple_model)

amount_coefs_full <- ggplot(coefs_posterior, aes(x = value, y = fct_rev(term_clean_fct))) +
  stat_density_ridges(aes(fill = model_name), alpha = 0.6, color = "black",
                      rel_min_height = 0.01, scale = 1.5, 
                      quantile_lines = TRUE, quantiles = 2) +
  geom_segment(data = coefs_summary, 
               aes(x = conf.low, xend = conf.high, 
                   y = fct_rev(term_clean_fct), yend = fct_rev(term_clean_fct)),
               size = 1) +
  geom_vline(xintercept = 0, linetype = "dotted", size = 1) + 
  scale_fill_manual(values = ngo_cols("red", "blue", name = FALSE), name = NULL) +
  labs(x = "Posterior median estimate + 90% credible interval", y = NULL,
       caption = "90% credible intervals shown in black. Solid vertical line = median; dotted vertical line = 0") +
  facet_wrap(~ full_model, scales = "free") + 
  theme_ngos() +
  theme(legend.position = "bottom",
        strip.text = element_blank())

amount_coefs_full %T>%
  print() %T>% 
  ggsave(., filename = here("output", "figures", "amount-coefs-full.pdf"),
         width = 8, height = 5, units = "in", device = cairo_pdf) %>% 
  ggsave(., filename = here("output", "figures", "amount-coefs-full.png"),
         width = 8, height = 5, units = "in", type = "cairo", dpi = 300)
```

## Predicted medians across different variables

**Charitable giving**

```{r plot-charity-amounts, fig.width=5, fig.height=4}
newdata_means <- results %>% 
  select(favor_humanitarian_bin, favor_human_rights_bin, favor_development_bin,
         give_charity_3, volunteer, political_knowledge_bin,
         ideology_bin, education_bin, religiosity_bin, income_bin, age_bin) %>% 
  summarize_all(typical) %>% 
  mutate(id = 1)

newdata_conditions_charity <- results %>% expand(nesting(crackdown, give_charity_3)) %>% 
  mutate(id = 1) %>% 
  left_join(select(newdata_means, -give_charity_3), by = "id") %>% 
  select(-id)

chains_fitted_charity <- posterior_linpred(m_amount_c_full, 
                                           newdata = newdata_conditions_charity)

coef_summary_charity <- newdata_conditions_charity %>% 
  bind_cols(tidyMCMC(chains_fitted_charity, estimate.method = "median",
                     conf.int = TRUE, conf.level = 0.9, conf.method = "HPDinterval")) %>% 
  mutate_at(vars(crackdown, give_charity_3), funs(fct_inorder(., ordered = TRUE)))

ggplot(coef_summary_charity, aes(x = fct_rev(give_charity_3), y = estimate)) +
  geom_pointrange(aes(ymin = conf.low, ymax = conf.high, color = crackdown), 
                  position = position_dodge(width = 0.5)) +
  scale_color_manual(values = ngo_cols("orange", "green", name = FALSE), name = NULL) +
  scale_y_continuous(labels = dollar) +
  labs(x = "How often do you give to charity?", y = "Median amount donated") +
  theme_ngos() +
  theme(legend.position = "bottom",
        panel.grid.major.x = element_blank())
```

**Voluntarism and religiosity**

```{r plot-vol-relig-amounts}
newdata_conditions_vol_relig <- results %>% 
  expand(nesting(crackdown, volunteer, religiosity_bin)) %>% 
  filter(!is.na(religiosity_bin)) %>% 
  mutate(id = 1) %>% 
  left_join(select(newdata_means, -volunteer, -religiosity_bin), by = "id") %>% 
  select(-id)

chains_fitted_vol_relig <- posterior_linpred(m_amount_c_full, 
                                             newdata = newdata_conditions_vol_relig)

coef_summary_vol_relig <- newdata_conditions_vol_relig %>% 
  bind_cols(tidyMCMC(chains_fitted_vol_relig, estimate.method = "median",
                     conf.int = TRUE, conf.level = 0.9, conf.method = "HPDinterval")) %>% 
  mutate_at(vars(crackdown, volunteer, religiosity_bin), funs(fct_inorder(., ordered = TRUE))) %>% 
  mutate(religiosity_bin = fct_recode(religiosity_bin,
                                      `Rarely attend religious services` = "Rarely",
                                      `Attend religious services\nat least once a month` = 
                                        "At least once a month"))

ggplot(coef_summary_vol_relig, aes(x = fct_rev(volunteer), y = estimate)) +
  geom_pointrange(aes(ymin = conf.low, ymax = conf.high, color = crackdown), 
                  position = position_dodge(width = 0.5)) +
  scale_color_manual(values = ngo_cols("orange", "green", name = FALSE), name = NULL) +
  scale_y_continuous(labels = dollar) +
  labs(x = "Have you volunteered in the last 12 months?", y = "Median amount donated") +
  facet_wrap(~ religiosity_bin) +
  theme_ngos() +
  theme(legend.position = "bottom",
        panel.grid.major.x = element_blank())
```


\

# Likelihood of donation (full)

## Models

```{r build-models-likely-full, warning=FALSE, message=FALSE, cache=TRUE}
# Basic models
m_likely_c <- stan_glm(donate_likely_bin ~ crackdown,
                       data = results, family = binomial(link = "logit"),
                       prior = student_t(3, 0, 2.5),
                       prior_intercept = student_t(3, 0, 10),
                       chains = CHAINS, iter = ITER, warmup = WARMUP, seed = BAYES_SEED)

m_likely_ci <- update(m_likely_c, . ~ . + issue + crackdown * issue)

m_likely_cif <- update(m_likely_c, . ~ . + 
                         issue + funding + crackdown * issue + crackdown * funding + 
                         issue * funding + crackdown * issue * funding)

# Interaction models with extra controls
m_likely_c_full <- update(m_likely_c, . ~ . + 
                            favor_humanitarian_bin +
                            give_charity_3 + volunteer + political_knowledge_bin +
                            ideology_bin + education_bin + religiosity_bin +
                            income_bin + age_bin)

m_likely_ci_full <- update(m_likely_ci, . ~ . + 
                             favor_humanitarian_bin +
                             give_charity_3 + volunteer + political_knowledge_bin +
                             ideology_bin + education_bin + religiosity_bin +
                             income_bin + age_bin)

m_likely_cif_full <- update(m_likely_cif, . ~ . + 
                              favor_humanitarian_bin +
                              give_charity_3 + volunteer + political_knowledge_bin +
                              ideology_bin + education_bin + religiosity_bin +
                              income_bin + age_bin)
```

```{r tbl-models-likely-full, warning=FALSE, results="asis"}
huxreg(m_likely_c, m_likely_ci, m_likely_cif, 
       m_likely_c_full, m_likely_ci_full, m_likely_cif_full,
       coefs = clean_coefs_named,
       statistics = model_stats_bayes,
       stars = NULL) %T>% 
  print_hux() %>% 
  to_md(max_width = 100) %>% 
  cat(file = here("output", "tables", "tbl-models-likelihood-full.md"))
```

## Coefficient plot

```{r models-coefs-plot-likely, warning=FALSE, message=FALSE, fig.width=8, fig.height=5}
posterior_details_likely <- data_frame(model_name = c("Basic model", "Full model"),
                                       model = list(m_likely_cif,
                                                    m_likely_cif_full)) %>% 
  mutate(posterior = model %>% map(~ as_data_frame(.)),
         tidy_summary = model %>% map(~ tidyMCMC(., conf.int = TRUE, conf.level = 0.9,
                                                 conf.method = "HPDinterval"))) 

coefs_posterior_likely <- posterior_details_likely %>%
  unnest(posterior) %>% 
  gather(term, value, -model_name) %>% 
  left_join(clean_coefs, by = "term") %>% 
  filter(term_clean != "Intercept") %>% 
  mutate(full_model = !simple_model)

coefs_summary_likely <- posterior_details_likely %>% 
  unnest(tidy_summary) %>% 
  left_join(clean_coefs, by = "term") %>% 
  filter(term_clean != "Intercept") %>% 
  mutate(full_model = !simple_model)

likely_coefs_full <- ggplot(coefs_posterior_likely, aes(x = value, y = fct_rev(term_clean_fct))) +
  stat_density_ridges(aes(fill = model_name), alpha = 0.6, color = "black",
                      rel_min_height = 0.01, scale = 1.5, 
                      quantile_lines = TRUE, quantiles = 2) +
  geom_segment(data = coefs_summary_likely, 
               aes(x = conf.low, xend = conf.high, 
                   y = fct_rev(term_clean_fct), yend = fct_rev(term_clean_fct)),
               size = 1) +
  geom_vline(xintercept = 0, linetype = "dotted", size = 1) + 
  scale_fill_manual(values = ngo_cols("red", "blue", name = FALSE), name = NULL) +
  labs(x = "Posterior median estimate + 90% credible interval", y = NULL,
       caption = "90% credible intervals shown in black. Solid vertical line = median; dotted vertical line = 0") +
  facet_wrap(~ full_model, scales = "free") + 
  theme_ngos() +
  theme(legend.position = "bottom",
        strip.text = element_blank())

likely_coefs_full %T>%
  print() %T>% 
  ggsave(., filename = here("output", "figures", "likely-coefs-full.pdf"),
         width = 8, height = 5, units = "in", device = cairo_pdf) %>% 
  ggsave(., filename = here("output", "figures", "likely-coefs-full.png"),
         width = 8, height = 5, units = "in", type = "cairo", dpi = 300)
```


# Original computing environment

<button data-toggle="collapse" data-target="#sessioninfo" class="btn btn-primary btn-md btn-info">Here's what we used the last time we built this page</button>

<div id="sessioninfo" class="collapse">

```{r show-session-info, echo=TRUE}
writeLines(readLines(file.path(Sys.getenv("HOME"), ".R/Makevars")))

devtools::session_info()
```

</div> 
